一种基于脑机接口的抗肩冲浪图像认证方案

Florian Gondesen, Matthias Marx, Ann-Christine Kycler
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引用次数: 4

摘要

随着消费者脑机接口的日益普及,可以考虑新的认证方法。在本文中,我们提出了一种抗肩冲浪的方法,通过测量大脑活动来输入图形密码。密码是通过快速连续视觉呈现反复显示的图像子集。密码图像的出现需要在脑电图中产生事件相关电位,即P300反应。P300响应用于对图像是否属于密码子集进行分类。我们比较单个分类器,与特定用户的样本训练,一般的P300分类器,训练在所有科目。我们在随后的三次实验中评估分类结果的持久性。从第一次到第三次,分类得分显著增加。比较使用自然照片和简单物体作为刺激没有显著差异。总的来说,我们的认证方案实现了大约10%的错误率。在未来,随着准确性的提高和普及,脑机接口可以在替代身份验证方法中找到实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Shoulder-Surfing Resistant Image-Based Authentication Scheme with a Brain-Computer Interface
With the increasing availability of consumer brain-computer interfaces, new methods of authentication can be considered. In this paper, we present a shoulder surfing resistant means of entering a graphical password by measuring brain activity. The password is a subset of images displayed repeatedly by rapid serial visual presentation. The occurrence of a password image entails an event-related potential in the electroencephalogram, the P300 response. The P300 response is used to classify whether an image belongs to the password subset or not. We compare individual classifiers, trained with samples of a specific user, to general P300 classifiers, trained over all subjects. We evaluate the permanence of the classification results in three subsequent experiment sessions. The classification score significantly increases from the first to the third session. Comparing the use of natural photos or simple objects as stimuli shows no significant difference. In total, our authentication scheme achieves an equal error rate of about 10%. In the future, with increasing accuracy and proliferation, brain-computer interfaces could find practical application in alternative authentication methods.
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